Overview

Dataset statistics

Number of variables9
Number of observations699
Missing cells0
Missing cells (%)0.0%
Duplicate rows46
Duplicate rows (%)6.6%
Total size in memory49.3 KiB
Average record size in memory72.2 B

Variable types

Numeric9

Alerts

Dataset has 46 (6.6%) duplicate rowsDuplicates
1 is highly overall correlated with 2 and 6 other fieldsHigh correlation
2 is highly overall correlated with 1 and 7 other fieldsHigh correlation
3 is highly overall correlated with 1 and 6 other fieldsHigh correlation
4 is highly overall correlated with 1 and 6 other fieldsHigh correlation
5 is highly overall correlated with 1 and 6 other fieldsHigh correlation
6 is highly overall correlated with 1 and 6 other fieldsHigh correlation
7 is highly overall correlated with 1 and 6 other fieldsHigh correlation
8 is highly overall correlated with 1 and 7 other fieldsHigh correlation
9 is highly overall correlated with 2 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-03-26 11:36:24.593321
Analysis finished2023-03-26 11:36:36.288896
Duration11.7 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

1
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4177396
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-03-26T17:06:36.330694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8157407
Coefficient of variation (CV)0.63737135
Kurtosis-0.62371541
Mean4.4177396
Median Absolute Deviation (MAD)2
Skewness0.59285853
Sum3088
Variance7.9283955
MonotonicityNot monotonic
2023-03-26T17:06:36.420035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 145
20.7%
5 130
18.6%
3 108
15.5%
4 80
11.4%
10 69
9.9%
2 50
 
7.2%
8 46
 
6.6%
6 34
 
4.9%
7 23
 
3.3%
9 14
 
2.0%
ValueCountFrequency (%)
1 145
20.7%
2 50
 
7.2%
3 108
15.5%
4 80
11.4%
5 130
18.6%
6 34
 
4.9%
7 23
 
3.3%
8 46
 
6.6%
9 14
 
2.0%
10 69
9.9%
ValueCountFrequency (%)
10 69
9.9%
9 14
 
2.0%
8 46
 
6.6%
7 23
 
3.3%
6 34
 
4.9%
5 130
18.6%
4 80
11.4%
3 108
15.5%
2 50
 
7.2%
1 145
20.7%

2
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1344778
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-03-26T17:06:36.518002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0514591
Coefficient of variation (CV)0.97351434
Kurtosis0.098802885
Mean3.1344778
Median Absolute Deviation (MAD)0
Skewness1.2331366
Sum2191
Variance9.3114027
MonotonicityNot monotonic
2023-03-26T17:06:36.598961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 384
54.9%
10 67
 
9.6%
3 52
 
7.4%
2 45
 
6.4%
4 40
 
5.7%
5 30
 
4.3%
8 29
 
4.1%
6 27
 
3.9%
7 19
 
2.7%
9 6
 
0.9%
ValueCountFrequency (%)
1 384
54.9%
2 45
 
6.4%
3 52
 
7.4%
4 40
 
5.7%
5 30
 
4.3%
6 27
 
3.9%
7 19
 
2.7%
8 29
 
4.1%
9 6
 
0.9%
10 67
 
9.6%
ValueCountFrequency (%)
10 67
 
9.6%
9 6
 
0.9%
8 29
 
4.1%
7 19
 
2.7%
6 27
 
3.9%
5 30
 
4.3%
4 40
 
5.7%
3 52
 
7.4%
2 45
 
6.4%
1 384
54.9%

3
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2074392
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-03-26T17:06:36.685592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9719128
Coefficient of variation (CV)0.9265687
Kurtosis0.00701098
Mean3.2074392
Median Absolute Deviation (MAD)0
Skewness1.1618592
Sum2242
Variance8.8322655
MonotonicityNot monotonic
2023-03-26T17:06:36.768667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 353
50.5%
2 59
 
8.4%
10 58
 
8.3%
3 56
 
8.0%
4 44
 
6.3%
5 34
 
4.9%
6 30
 
4.3%
7 30
 
4.3%
8 28
 
4.0%
9 7
 
1.0%
ValueCountFrequency (%)
1 353
50.5%
2 59
 
8.4%
3 56
 
8.0%
4 44
 
6.3%
5 34
 
4.9%
6 30
 
4.3%
7 30
 
4.3%
8 28
 
4.0%
9 7
 
1.0%
10 58
 
8.3%
ValueCountFrequency (%)
10 58
 
8.3%
9 7
 
1.0%
8 28
 
4.0%
7 30
 
4.3%
6 30
 
4.3%
5 34
 
4.9%
4 44
 
6.3%
3 56
 
8.0%
2 59
 
8.4%
1 353
50.5%

4
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.806867
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-03-26T17:06:36.880351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8553792
Coefficient of variation (CV)1.0172834
Kurtosis0.98794707
Mean2.806867
Median Absolute Deviation (MAD)0
Skewness1.5244681
Sum1962
Variance8.1531906
MonotonicityNot monotonic
2023-03-26T17:06:36.959941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 407
58.2%
3 58
 
8.3%
2 58
 
8.3%
10 55
 
7.9%
4 33
 
4.7%
8 25
 
3.6%
5 23
 
3.3%
6 22
 
3.1%
7 13
 
1.9%
9 5
 
0.7%
ValueCountFrequency (%)
1 407
58.2%
2 58
 
8.3%
3 58
 
8.3%
4 33
 
4.7%
5 23
 
3.3%
6 22
 
3.1%
7 13
 
1.9%
8 25
 
3.6%
9 5
 
0.7%
10 55
 
7.9%
ValueCountFrequency (%)
10 55
 
7.9%
9 5
 
0.7%
8 25
 
3.6%
7 13
 
1.9%
6 22
 
3.1%
5 23
 
3.3%
4 33
 
4.7%
3 58
 
8.3%
2 58
 
8.3%
1 407
58.2%

5
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2160229
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-03-26T17:06:37.045024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2142999
Coefficient of variation (CV)0.68852118
Kurtosis2.1690664
Mean3.2160229
Median Absolute Deviation (MAD)0
Skewness1.7121718
Sum2248
Variance4.903124
MonotonicityNot monotonic
2023-03-26T17:06:37.131480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 386
55.2%
3 72
 
10.3%
4 48
 
6.9%
1 47
 
6.7%
6 41
 
5.9%
5 39
 
5.6%
10 31
 
4.4%
8 21
 
3.0%
7 12
 
1.7%
9 2
 
0.3%
ValueCountFrequency (%)
1 47
 
6.7%
2 386
55.2%
3 72
 
10.3%
4 48
 
6.9%
5 39
 
5.6%
6 41
 
5.9%
7 12
 
1.7%
8 21
 
3.0%
9 2
 
0.3%
10 31
 
4.4%
ValueCountFrequency (%)
10 31
 
4.4%
9 2
 
0.3%
8 21
 
3.0%
7 12
 
1.7%
6 41
 
5.9%
5 39
 
5.6%
4 48
 
6.9%
3 72
 
10.3%
2 386
55.2%
1 47
 
6.7%

6
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4864092
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-03-26T17:06:37.220637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.6219288
Coefficient of variation (CV)1.0388708
Kurtosis-0.72646662
Mean3.4864092
Median Absolute Deviation (MAD)0
Skewness1.0253473
Sum2437
Variance13.118368
MonotonicityNot monotonic
2023-03-26T17:06:37.299919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 418
59.8%
10 132
 
18.9%
2 30
 
4.3%
5 30
 
4.3%
3 28
 
4.0%
8 21
 
3.0%
4 19
 
2.7%
9 9
 
1.3%
7 8
 
1.1%
6 4
 
0.6%
ValueCountFrequency (%)
1 418
59.8%
2 30
 
4.3%
3 28
 
4.0%
4 19
 
2.7%
5 30
 
4.3%
6 4
 
0.6%
7 8
 
1.1%
8 21
 
3.0%
9 9
 
1.3%
10 132
 
18.9%
ValueCountFrequency (%)
10 132
 
18.9%
9 9
 
1.3%
8 21
 
3.0%
7 8
 
1.1%
6 4
 
0.6%
5 30
 
4.3%
4 19
 
2.7%
3 28
 
4.0%
2 30
 
4.3%
1 418
59.8%

7
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4377682
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-03-26T17:06:37.383238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4383643
Coefficient of variation (CV)0.70928698
Kurtosis0.18462131
Mean3.4377682
Median Absolute Deviation (MAD)1
Skewness1.0999691
Sum2403
Variance5.9456202
MonotonicityNot monotonic
2023-03-26T17:06:37.472137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 166
23.7%
3 165
23.6%
1 152
21.7%
7 73
10.4%
4 40
 
5.7%
5 34
 
4.9%
8 28
 
4.0%
10 20
 
2.9%
9 11
 
1.6%
6 10
 
1.4%
ValueCountFrequency (%)
1 152
21.7%
2 166
23.7%
3 165
23.6%
4 40
 
5.7%
5 34
 
4.9%
6 10
 
1.4%
7 73
10.4%
8 28
 
4.0%
9 11
 
1.6%
10 20
 
2.9%
ValueCountFrequency (%)
10 20
 
2.9%
9 11
 
1.6%
8 28
 
4.0%
7 73
10.4%
6 10
 
1.4%
5 34
 
4.9%
4 40
 
5.7%
3 165
23.6%
2 166
23.7%
1 152
21.7%

8
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8669528
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-03-26T17:06:37.603584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0536339
Coefficient of variation (CV)1.0651148
Kurtosis0.47426868
Mean2.8669528
Median Absolute Deviation (MAD)0
Skewness1.4222613
Sum2004
Variance9.32468
MonotonicityNot monotonic
2023-03-26T17:06:37.690130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 443
63.4%
10 61
 
8.7%
3 44
 
6.3%
2 36
 
5.2%
8 24
 
3.4%
6 22
 
3.1%
5 19
 
2.7%
4 18
 
2.6%
7 16
 
2.3%
9 16
 
2.3%
ValueCountFrequency (%)
1 443
63.4%
2 36
 
5.2%
3 44
 
6.3%
4 18
 
2.6%
5 19
 
2.7%
6 22
 
3.1%
7 16
 
2.3%
8 24
 
3.4%
9 16
 
2.3%
10 61
 
8.7%
ValueCountFrequency (%)
10 61
 
8.7%
9 16
 
2.3%
8 24
 
3.4%
7 16
 
2.3%
6 22
 
3.1%
5 19
 
2.7%
4 18
 
2.6%
3 44
 
6.3%
2 36
 
5.2%
1 443
63.4%

9
Real number (ℝ)

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5894134
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-03-26T17:06:37.775850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7150779
Coefficient of variation (CV)1.0790634
Kurtosis12.657878
Mean1.5894134
Median Absolute Deviation (MAD)0
Skewness3.5606578
Sum1111
Variance2.9414923
MonotonicityNot monotonic
2023-03-26T17:06:37.859154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 579
82.8%
2 35
 
5.0%
3 33
 
4.7%
10 14
 
2.0%
4 12
 
1.7%
7 9
 
1.3%
8 8
 
1.1%
5 6
 
0.9%
6 3
 
0.4%
ValueCountFrequency (%)
1 579
82.8%
2 35
 
5.0%
3 33
 
4.7%
4 12
 
1.7%
5 6
 
0.9%
6 3
 
0.4%
7 9
 
1.3%
8 8
 
1.1%
10 14
 
2.0%
ValueCountFrequency (%)
10 14
 
2.0%
8 8
 
1.1%
7 9
 
1.3%
6 3
 
0.4%
5 6
 
0.9%
4 12
 
1.7%
3 33
 
4.7%
2 35
 
5.0%
1 579
82.8%

Interactions

2023-03-26T17:06:35.125885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:24.781502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:26.317370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:28.015407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:29.953661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:31.432556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:32.537951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:33.552396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:34.345262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:35.215079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:24.966520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:26.510892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:28.260170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:30.113303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:31.589618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:32.637749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:33.641601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:34.433064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:35.302425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:25.126437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:26.648459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:28.453492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:30.287427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:31.741502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:32.819583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:33.736626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:34.520589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:35.442510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:25.295875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:26.795263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:28.681581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:30.516273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:31.891161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:32.929513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:33.820513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:34.607192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:35.569975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:25.470474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:27.048876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:28.924131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:30.689051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:32.069332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:33.032958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:33.908214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:34.698071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:35.658318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:25.655420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:27.224816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:29.196213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:30.830378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:32.173627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:33.145501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:33.993966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:34.786098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:35.747181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:25.823923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:27.391437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:29.410179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:30.986654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:32.273399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:33.253358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:34.082307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:34.870528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:35.837583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:25.985669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:27.561106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:29.571369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:31.157002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:32.362659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:33.366954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:34.172579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:34.957665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:35.924701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:26.151778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:27.797038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:29.755722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:31.310847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:32.454086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:33.459060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:34.256674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-26T17:06:35.041093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-26T17:06:37.944898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
123456789
11.0000.6660.6640.5420.5840.5860.5380.5700.419
20.6661.0000.8920.7430.7870.7610.7190.7570.509
30.6640.8921.0000.7120.7590.7410.6920.7250.473
40.5420.7430.7121.0000.6680.6940.6250.6340.447
50.5840.7870.7590.6681.0000.6890.6400.7060.480
60.5860.7610.7410.6940.6891.0000.6690.6490.478
70.5380.7190.6920.6250.6400.6691.0000.6620.387
80.5700.7570.7250.6340.7060.6490.6621.0000.504
90.4190.5090.4730.4470.4800.4780.3870.5041.000

Missing values

2023-03-26T17:06:36.050252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-26T17:06:36.229345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

123456789
0511121311
15445710321
2311122311
3688134371
4411321311
5810108710971
61111210311
7212121311
8211121115
9421121211
123456789
689111121118
690111321111
69151010545441
692311121111
693311121212
694311132111
695211121111
696510103738102
6974864341061
6984885451041

Duplicate rows

Most frequently occurring

123456789# duplicates
311112111127
511112131123
411112121122
1931112121120
1831112111112
2031112131112
1221112111110
2641112111110
2741112121110
3551112121110